U.S. patent application number 17/003562 was filed with the patent office on 2021-05-20 for facility failure prediction system and method for using acoustic signal of ultrasonic band.
This patent application is currently assigned to MOVIC LAB INC.. The applicant listed for this patent is MOVIC LAB INC.. Invention is credited to Won Keun LEE.
Application Number | 20210149387 17/003562 |
Document ID | / |
Family ID | 1000005077543 |
Filed Date | 2021-05-20 |
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United States Patent
Application |
20210149387 |
Kind Code |
A1 |
LEE; Won Keun |
May 20, 2021 |
FACILITY FAILURE PREDICTION SYSTEM AND METHOD FOR USING ACOUSTIC
SIGNAL OF ULTRASONIC BAND
Abstract
Disclosed are a facility failure prediction system and method
using an acoustic signal of the ultrasonic band. The facility
failure prediction system using an ultrasonic band according to the
present disclosure includes a detection sensor located adjacent to
a facility, and a server configured to determine whether sampling
data extracted from an acoustic signal is a normal signal or an
abnormal signal to generate a plurality of labeling information, to
analyze sampling data corresponding to abnormal signal labeling
information determined as an abnormal signal to generate abnormal
signal analysis information, and to analyze a pattern of normal
signal labeling information determined as the normal signal among
the plurality of labeling information and the abnormal signal
analysis information to provide failure prediction information for
the facility.
Inventors: |
LEE; Won Keun; (Incheon,
KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MOVIC LAB INC. |
Incheon |
|
KR |
|
|
Assignee: |
MOVIC LAB INC.
Incheon
KR
|
Family ID: |
1000005077543 |
Appl. No.: |
17/003562 |
Filed: |
August 26, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
PCT/KR2019/017803 |
Dec 16, 2019 |
|
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17003562 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 29/12 20130101;
G05B 23/0283 20130101 |
International
Class: |
G05B 23/02 20060101
G05B023/02; G01N 29/12 20060101 G01N029/12 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 18, 2019 |
KR |
10-2019-0147610 |
Claims
1. A facility failure prediction system comprising: a detection
sensor positioned adjacent to a facility and configured to collect
an acoustic signal from sound generated when the facility operates;
a sampling data extractor configured to sample the acoustic signal,
to cancel noise, and to extract sampling data; a signal
discriminator configured to determine whether the sampling data is
a normal signal or an abnormal signal and to generate a plurality
of labeling information; an abnormal signal analyzer configured to
analyze the sampling data corresponding to abnormal signal labeling
information determined as an abnormal signal among the plurality of
labeling information and to generate abnormal signal analysis
information; and a pattern analyzer configured to analyze a pattern
of normal signal labeling information determined as the normal
signal among the plurality of labeling information and the abnormal
signal analysis information and to provide failure prediction
information for the facility.
2. The facility failure prediction system of claim 1, further
comprising: a database (DB) unit including a sampling data DB
configured to store the sampling data; and a detected signal
information DB configured to store labeling information in which
the sampling data is determined as a normal signal or an abnormal
signal.
3. The facility failure prediction system of claim 2, wherein the
signal discriminator, the abnormal signal analyzer, and the pattern
analyzer are provided in one server.
4. The facility failure prediction system of claim 3, wherein the
sampling data input to the signal discriminator is information
subdivided based on a predetermined time.
5. The facility failure prediction system of claim 3, wherein the
abnormal signal analysis information output from the abnormal
signal analyzer comprises values obtained by analyzing a kind and
level of an abnormality type for sampling data corresponding to the
abnormal signal labeling information.
6. The facility failure prediction system of claim 3, wherein the
pattern analyzer receives the normal signal labeling information
from the detected signal information DB, receives the abnormal
signal analysis information from the abnormal signal analyzer, and
analyzes a pattern of the information.
7. The facility failure prediction system of claim 3, wherein the
pattern analyzer analyzes the pattern by arranging the normal
signal labeling information and the abnormal signal analysis
information in a time-series manner in a time-ordered sequence.
8. The facility failure prediction system of claim 1, wherein the
detection sensor is spaced apart from the facility.
9. The facility failure prediction system of claim 1, wherein the
detection sensor converts the collected acoustic signal into a
digital signal, and a sampling rate for converting the acoustic
signal into the digital signal is higher than 35 kHz.
10. The facility failure prediction system of claim 9, wherein the
sampling rate is 35 kHz to 300 kHz.
11. The facility failure prediction system of claim 3, wherein
either the detection sensor or the server filters an ultrasonic
band signal from the collected acoustic signal.
12. The facility failure prediction system of claim 11, wherein
either the detection sensor or the server extracts the sampling
data by canceling noise from the extracted ultrasonic band
signal.
13. A facility failure prediction method comprising: a sampling
data extraction operation of sampling an acoustic signal generated
in a facility and canceling noise to extract sampling data; a
signal determination operation of determining whether the sampling
data is a normal signal or an abnormal signal and generating and
storing labeling information; an abnormal signal analysis operation
of receiving and analyzing sampling data corresponding to abnormal
signal labeling information determined as an abnormal signal among
the labeling information and outputting abnormal signal analysis
information; and a pattern analysis operation of analyzing a
pattern of a facility failure from the normal signal labeling
information determined as the normal signal among the plurality of
labeling information and the abnormal signal analysis
information.
14. The facility failure prediction method of claim 13, wherein, in
the signal determination operation, sampling data subdivided based
on a predetermined time is used.
15. The facility failure prediction method of claim 13, wherein the
abnormal signal analysis information comprises values obtained by
analyzing a kind and level of an abnormality type for sampling data
corresponding to the abnormal signal labeling information.
16. The facility failure prediction method of claim 13, wherein, in
the pattern analysis operation, the pattern is analyzed by
arranging the normal signal labeling information and the abnormal
signal analysis information in a time-series manner in time-ordered
sequence.
17. The facility failure prediction method of claim 13, further
comprising: a sound wave collection operation of collecting an
acoustic signal from sound generated when the facility operates,
before the signal determination operation; converting the acoustic
signal into a digital signal; and an ultrasonic band filtering
operation of filtering the collected acoustic signal to extract an
ultrasonic band signal.
18. The facility failure prediction method of claim 17, wherein, in
converting the acoustic signal into the digital signal, a sampling
rate for converting the acoustic signal into the digital signal is
higher than 35 kHz.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of International
Patent Application No. PCT/KR2019/017803, filed on Dec. 16, 2019,
which is based upon and claims the benefit of priority to Korean
Patent Application No. 10-2019-0147610, filed on Nov. 18, 2019. The
disclosures of the above-listed applications are hereby
incorporated by reference herein in their entirety.
TECHNICAL FIELD
[0002] The present disclosure relates to a facility failure
prediction system and method using the ultrasonic band.
[0003] More specifically, the present disclosure relates to a
facility failure prediction system and method for providing failure
prediction information for a facility using signals of ultrasonic
bands among acoustic signals generated at the facility.
BACKGROUND ART
[0004] In general, a facility line of a facility which needs to be
operated stably and continuously in an industrial site may be
stopped due to sudden breakdown that causes fatal damage to the
facility line itself and an unexpected failure to decrease in
production, and thus, in order to prevent this, methods for
predicting facility failure have been studied and developed.
[0005] The current failure prediction method includes a method of
detecting a change in an abnormal state of a facility, detecting a
defect occurring here, determining a state of the facility or a
component, and predicting the life of the facility.
[0006] However, in the related art facility failure prediction
system or method, in many cases, a sensor that senses a signal
related to a defect in a facility is directly attached to the
facility to measure a change in sound, vibration, heat, or current
to detect a problem of the facility.
[0007] However, in the case of the sensor attached to the facility,
continuous vibration of the facility acts as an element that
interferes with sensor detection, causing problems in accurate
failure prediction.
[0008] In addition, as a failure prediction system for detecting a
problem in a plane by sensing a sound during operation of the
facility, there is an existing case in which an abnormal sound of a
facility is detected within an audible frequency. In this case,
however, since an abnormal sound of the facility is detected only
with the audible frequency, accuracy and perceived speed of failure
prediction is lowered.
DISCLOSURE
Technical Problem
[0009] An aspect of the present disclosure is to provide a facility
failure prediction system and method.
[0010] More specifically, the present disclosure provides a
facility failure prediction system and method for providing failure
prediction information for a facility using signals of ultrasonic
bands among acoustic signals generated at the facility.
Technical Solution
[0011] According to an aspect of the present disclosure, a facility
failure prediction system includes: a detection sensor positioned
adjacent to a facility and configured to collect an acoustic signal
from sound generated when the facility operates; a sampling data
extractor configured to sample the acoustic signal, to cancel
noise, and to extract sampling data; a signal discriminator
configured to determine whether the sampling data is a normal
signal or an abnormal signal and to generate a plurality of
labeling information; an abnormal signal analyzer configured to
analyze the sampling data corresponding to abnormal signal labeling
information determined as an abnormal signal among the plurality of
labeling information and to generate abnormal signal analysis
information; and a pattern analyzer configured to analyze a pattern
of normal signal labeling information determined as the normal
signal among the plurality of labeling information and the abnormal
signal analysis information and to provide failure prediction
information for the facility.
[0012] The facility failure prediction system may further include:
a database (DB) unit including a sampling data DB configured to
store the sampling data; and a detected signal information DB
configured to store labeling information in which the sampling data
is determined as a normal signal or an abnormal signal.
[0013] The signal discriminator, the abnormal signal analyzer, and
the pattern analyzer may be provided in one server.
[0014] The sampling data input to the signal discriminator may be
information subdivided based on a predetermined time.
[0015] The abnormal signal analysis information output from the
abnormal signal analyzer may include values obtained by analyzing a
kind and level of an abnormality type for sampling data
corresponding to the abnormal signal labeling information.
[0016] The pattern analyzer may receive the normal signal labeling
information from the detected signal information DB, receive the
abnormal signal analysis information from the abnormal signal
analyzer, and analyze a pattern of the information.
[0017] The pattern analyzer may analyze the pattern by arranging
the normal signal labeling information and the abnormal signal
analysis information in a time-series manner in a time-ordered
sequence.
[0018] The detection sensor may be spaced apart from the
facility.
[0019] The detection sensor may convert the collected acoustic
signal into a digital signal, a sampling rate for converting the
acoustic signal into the digital signal may be higher than 35 kHz,
and the sampling rate may be 35 kHz to 300 kHz, for example.
[0020] Either the detection sensor or the server may filter an
ultrasonic band signal from the collected acoustic signal.
[0021] Either the detection sensor or the server may extract the
sampling data by canceling noise from the extracted ultrasonic band
signal.
[0022] According to another aspect of the present disclosure, a
facility failure prediction method includes: a sampling data
extraction operation of sampling an acoustic signal generated in a
facility and canceling noise to extract sampling data; a signal
determination operation of determining whether the sampling data is
a normal signal or an abnormal signal and generating and storing
labeling information; an abnormal signal analysis operation of
receiving and analyzing sampling data corresponding to abnormal
signal labeling information determined as an abnormal signal among
the labeling information and outputting abnormal signal analysis
information; and a pattern analysis operation of analyzing a
pattern of a facility failure from the normal signal labeling
information determined as the normal signal among the plurality of
labeling information and the abnormal signal analysis
information.
[0023] In the signal determination operation, sampling data
subdivided based on a predetermined time may be used.
[0024] The abnormal signal analysis information may include a value
obtained by analyzing a kind and level of an abnormality type for
sampling data corresponding to the abnormal signal labeling
information.
[0025] In the pattern analysis operation, the pattern may be
analyzed by arranging the normal signal labeling information and
the abnormal signal analysis information in a time-series manner in
a time-ordered sequence.
[0026] The facility failure method may further include: a sound
wave collection operation of collecting an acoustic signal from
sound generated when the facility operates before the signal
determination operation; and an ultrasonic band filtering operation
of filtering the collected acoustic signal to extract an ultrasonic
band signal.
[0027] In the sound wave collection operation, the collected
acoustic signal may be converted into a digital signal, and a
sampling rate for converting the acoustic signal into the digital
signal may be higher than 35 kHz.
Advantageous Effects
[0028] According to the facility failure prediction system and
method of an example of the present disclosure, failure prediction
information for a facility is provided using an ultrasonic band
signal among acoustic signals generated during the operation of the
facility, so that a user may quickly handle the facility in a
factory, and thus, a cost of damage due to shutdown of the facility
may be minimized.
DESCRIPTION OF DRAWINGS
[0029] FIG. 1 is a view illustrating an outline of a facility
failure prediction system according to an embodiment of the present
disclosure.
[0030] FIG. 2 is a view illustrating an example of a detection
sensor 100 shown in FIG. 1.
[0031] FIG. 3 is a view illustrating an example of a server 200
shown in FIG. 1.
[0032] FIGS. 4 to 6 are views illustrating an example of a facility
failure prediction method according to an embodiment of the present
disclosure.
[0033] FIG. 7 is a view illustrating an example of failure
prediction information provided by a facility failure prediction
system according to an embodiment of the present disclosure.
BEST MODE FOR INVENTION
[0034] Hereinafter, embodiments of the present disclosure will be
described in detail with reference to the accompanying drawings. In
describing the present invention, if it is determined that a
detailed description of known functions and components associated
with the present invention unnecessarily obscure the gist of the
present invention, the detailed description thereof will be
omitted. The terms used henceforth are used to appropriately
express the embodiments of the present invention and may be altered
according to a person of a related field or conventional practice.
Therefore, the terms should be defined on the basis of the entire
content of this specification.
[0035] Hereinafter, a facility failure prediction system and a
facility failure prediction method according to the present
disclosure will be described with reference to the accompanying
drawings.
[0036] FIG. 1 is a view illustrating an outline of a facility
failure prediction system according to an embodiment of the present
disclosure.
[0037] A defect or damage that occurs in a facility 10 in the
manufacturing site may cause a shutdown of the facility 10, and the
amount of resultant economic damage is enormous, so it is
recognized to be important to take action before a failure occurs
to reduce cost.
[0038] Pant failure prediction is a technology for diagnosing a
state of the facility 10 in real-time to detect an abnormal
condition early and predict a failure that may occur in the future
to take appropriate measures, thereby improving the stability of
the facility 10.
[0039] In recent years, an industrial field to apply such facility
failure prediction has also expanded, and facility failure
prediction is expected to significantly contribute to production
sales.
[0040] To this end, the facility failure prediction system
according to an embodiment of the present disclosure may include a
detection sensor 100 and a server 200 required for prediction of
facility failure as illustrated in FIG. 1.
[0041] The detection sensor 100 may be located adjacent to the
facility 10, maybe physically spaced from the facility 10, and may
collect acoustic signals from sound generated when the facility 10
operates.
[0042] Since the detection sensor 100 according to an embodiment of
the present disclosure collects ultrasonic acoustic signals, the
detection sensor 100 does not need to be in direct contact with the
facility 10 and any impact that may occur during the operation of
the facility 10 is not transferred to the detection sensor 100, the
durability of the detection sensor 100 may be relatively
improved.
[0043] The acoustic signal may be signal-processed, sampled, and
filtered into an ultrasonic band signal, and the noise of the
ultrasonic band signal is canceled to obtain sampling data. The
server 200 may determine whether the sampling data is a normal
signal or an abnormal signal, analyze the sampling data for
abnormal signal labeling information determined as an abnormal
signal to generate abnormal signal analysis information, analyze a
time-series pattern of the abnormal signal analysis information,
and provides failure prediction information for the facility
10.
[0044] To this end, the server 200 (1) may generate a plurality of
labeling information by determining whether the sampling data is a
normal signal or an abnormal signal, (2) generate abnormal signal
analysis information analyzed for the sampling data corresponding
to abnormal signal labeling information determined as an abnormal
signal among the plurality of labeling information, and (3) analyze
a pattern of the normal signal labeling information determined as
the normal signal among the plurality of labeling information and
the abnormal signal analysis information, provide failure
prediction information for the facility to an administrator so that
the administrator may take measures in advance before the facility
10 is completely broken down.
[0045] Physical failure of the general mechanical facility 10 may
accompany damage to a connected portion, as well as a faulty
portion, and thus, the facility failure prediction system according
to an embodiment of the present disclosure is expected to reduce
repair costs through precautionary measures.
[0046] In addition, if a failure occurs in the facility 10, it
takes a considerable time to repair the malfunctioned facility 10,
and in this case, the facility failure prediction system of an
embodiment of the present disclosure may prevent failure of the
facility 10 through precautionary measures to eliminate a situation
in which a rate of operation is lowered, thus obtaining an effect
of improving productivity.
[0047] In addition, the facility failure prediction system
according to an embodiment of the present disclosure is capable of
proactive measures for abnormal conditions of the facility 10
through prediction of a facility failure, thereby preventing
product quality deterioration, which is one of the prognostic
symptoms of failure of the facility 10, and therefore, it may be
expected to maintain and improve quality of the products being
produced.
[0048] In such a facility failure prediction system, a
high-sensitivity audio card measurable for an ultrasonic band may
be included in the detection sensor 100 for fine detection.
[0049] In addition, for efficient data analysis, before analyzing
the acoustic signal, sampling data from which the noise of a
factory environment was canceled may be extracted, and machine
learning may be applied to determine whether the extracted sampling
data is a normal signal or an abnormal signal.
[0050] In addition, when it is determined that the sampling data is
an abnormal signal, abnormal signal analysis information regarding
a kind and level of an abnormality type of a facility that
generates the abnormal signal may be provided more accurately and
efficiently by applying deep learning algorithms by using sampling
data which is row data for the corresponding abnormal signal.
[0051] In addition, failure prediction information regarding the
facility may be provided by executing a pattern analysis program
previously installed in the server using the normal signal labeling
information determined as the normal signal among the plurality of
labeling information and the abnormal signal analysis
information.
[0052] Hereinafter, a configuration of the detection sensor 100 and
the server 200 applied to an example of the present disclosure will
be described in more detail.
[0053] FIG. 2 is a view illustrating an example of the detection
sensor 100 shown in FIG. 1, and FIG. 3 is a view illustrating an
example of the server 200 shown in FIG. 1.
[0054] In FIGS. 2 and 3, a case where the detection sensor 100
includes an ultrasonic band filtering module 130, and the server
200 does not have the ultrasonic band filtering module 130 is
illustrated as an example, but the present disclosure is not
limited thereto, and unlike the case of FIGS. 2 and 3, the
ultrasonic band filtering module 130 may be provided in the server
200 rather than in the detection sensor.
[0055] Hereinafter, for the purpose of description, as illustrated
in FIGS. 2 and 3, the case where the detection sensor 100 includes
the ultrasonic band filtering module 130 and the server 200 does
not have an ultrasonic band filtering module 130 will be described
as an example.
[0056] In addition, in FIGS. 2 and 3, a case where the server 200
includes a sampling data extractor 213 and the detection sensor 100
does not have the sampling data extractor 213 is illustrated as an
example, but the present disclosure is not limited thereto, and
unlike the case of FIGS. 2 and 3, the detection sensor 100 may
include the sampling data extractor 213 and the server 200 may not
have the sampling data extractor 213.
[0057] Hereinafter, for the purpose of description, as shown in
FIGS. 2 and 3, the case where the server 200 includes the sampling
data extractor 213 and the detection sensor 100 does not have the
sampling data extractor 213 will be described as an example.
[0058] In addition, in FIG. 3, a case where a signal discriminator
214, an abnormal signal analyzer 215, and a pattern analyzer 216
are provided in one server is illustrated as an example, but the
present disclosure is not limited thereto, and signal discriminator
214, the anomaly signal analyzer 215, and the pattern analyzer 216
may be spaced apart from each other and maybe provided in different
servers.
[0059] The detection sensor 100 may include a sensing unit 110, a
sampling unit 120, an ultrasonic band filtering module 130 and a
communication unit 140 to collect acoustic signals from sound
generated when the facility 10 operates.
[0060] The sensing unit 110, physically spaced apart from the
facility 10, may collect acoustic signals from sound generated when
the facility 10 operates. For example, the sensing unit 110 may
have a MEMS microcircuit.
[0061] The sampling unit 120 may serve to convert an acoustic
signal collected in the form of an analog signal from the sensing
unit 110 into a digital signal. The sampling unit 120 may have a
separate filter and an amplification circuit to enable high
sensitivity measurement and analysis, and a sampling rate for
converting the analog signal into the digital signal may be higher
than 35 kHz.
[0062] For example, the sampling rate may be 35 kHz to 300 kHz,
preferably 100 kHz to 300 kHz, and more preferably 190 kHz to 300
kHz.
[0063] The ultrasonic band filtering module 130 may filter and
extract an ultrasonic band signal from the digital acoustic signal
sampled with high sensitivity. That is, the ultrasonic band
filtering module 130 may remove a signal having a band lower than
an ultrasonic band from the acoustic signal and extract only an
acoustic signal having an ultrasonic band.
[0064] The extracted acoustic signal having the ultrasonic band
maybe 35 kHz or higher and may include an acoustic signal having a
band of 35 kHz to 300 kHz band, for example.
[0065] The communication unit 140 may transmit the acoustic signal
collected by the detection sensor 100 to the server 200 through
wirelessly or by a wire. In FIG. 1, a case where the detection
sensor 100 and the server are connected by a wire is illustrated,
but the present disclosure is not limited thereto and the detection
sensor 100 and the server 200 may interwork with each other via the
Internet.
[0066] As shown in FIG. 3, the server 200 may include a controller
210 and a database (DB) unit 220.
[0067] The DB unit 220 may store the sampling data and perform
signal determination on sampled data, so that labeling information,
which is data information determined as a normal signal or an
abnormal signal, may be stored.
[0068] To this end, the DB unit 220 may include a sampling data DB
221 storing sampling data having a row-data form for a target
signal and a detected signal information DB 223 storing labeling
information generated after the signal determination was performed
on the sampling data.
[0069] Here, the sampling data stored in the DB unit 220 may have a
row-data form for a predetermined time reference regarding the
digital signal converted from an analog signal after the analog
signal was sampled.
[0070] Each of the sampling data may be subdivided into a
predetermined time reference and stored. For example, if the
subdivided time reference is determined in units of 0.5 msec, the
sampling data stored in the sampling data DB 221 may have a time
length of 0.5 msec, and if the subdivided time reference is 1
second, the sampling data stored in the sampling data DB 221 may
have a time length of 1 second.
[0071] As described above, the sampling data stored in the sampling
data DB 221 may include information on a waveform of the
corresponding signal and time information on the sampling data
during a predetermined time reference.
[0072] As such, the time length of the sampling data stored in the
sampling data DB 221 may be previously set in the server and may be
changed by a server administrator for optimization.
[0073] The sampling data DB 221 may be used for training and
testing of sampling data initially for signal determination and may
be used throughout during the operation of the facility failure
prediction system after an initial operation, and may be used to
determine whether sampling data is abnormal.
[0074] The detected signal information DB 223 may store labeling
information obtained by the signal determination on the sampling
data stored in the sampling data DB 221.
[0075] Since the labeling information is signal labeling
information determined for each of a plurality of sampling data
stored in the sampling data DB 221, each labeling information may
include labeling information regarding whether corresponding
sampling data is a normal signal or an abnormal signal and
identification information or time information for sampling data
itself.
[0076] For example, if the sampling data is determined as a normal
signal, normal signal labeling information determined as a normal
signal for the corresponding sampling data may be stored, and if
the sampling data is determined as an abnormal signal, abnormal
signal labeling information determined as an abnormal signal for
the corresponding sampling data may be stored.
[0077] Accordingly, the sum of the number of normal signal labeling
information and the number of abnormal signal labeling information
stored in the detected signal information DB 223 may be equal to
the sum of a total number of sampling data stored in the sampling
data DB 221, and the normal signal labeling information and the
abnormal signal labeling information may match the sampling data,
respectively.
[0078] Therefore, if the server selects any one abnormal signal
labeling information from the detected signal information DB 223,
sampling data having a raw data form for the corresponding abnormal
signal labeling information may also be selected from the sampling
data DB 221.
[0079] The controller 210 may extract sampling data, which has an
ultrasonic band, from the acoustic signal, determine whether
signal-processed data is a normal signal or an abnormal signal,
perform abnormal signal analysis on sampling data determined as an
abnormal signal, generate abnormal signal analysis information for
the corresponding sampling data, arrange normal signal sampling
information and the abnormal signal analysis in a time-series
manner, analyze time-series patterns of the normal signal sampling
information and the abnormal signal analysis information, and
provide prediction information regarding the failure of the
facility.
[0080] To this end, the controller 210 may include a communication
module 211, a sampling data extractor 213, an abnormal signal
discriminator 214, an abnormal signal analyzer 215, a pattern
analyzer 216, and an output module 217.
[0081] The communication module 211 may be connected to the
detection sensor 100 by wire or wirelessly and receive an acoustic
signal having an ultrasonic band transmitted from the detection
sensor 100. As described above, the acoustic signal input to the
communication module of the server may be, for example, data
information on a signal which has been sampled and filtered to an
ultrasonic band.
[0082] The sampling data extractor 213 may extract sampling data by
canceling noise from the ultrasonic band signal input through the
communication module. Specifically, the acoustic signal coming from
a factory environment where the facility 10 is located may be an
acoustic signal with unnecessary noise information such as the
mechanical sound of other machines or parts and human noise of the
facility 10 synthesized therein.
[0083] The sampling data extractor 213 may filter and cancel noise
from the acoustic signal to thereby extract sampling data from the
acoustic signal generated by a specific part of the facility 10,
and use the extracted sampling data in data analysis to thereby
increasing the accuracy of the analysis.
[0084] As described above, the controller may sample the acoustic
signal measured in the facility, perform ultrasonic band filtering,
and determine and analyze sampling data in the form of row data
without noise, analyze a pattern in a time-series manner, and
provide prediction information regarding the facility to the user
or the administrator.
[0085] In order to perform these operations, the controller may
include the signal discriminator 214, the abnormal signal analyzer
215, and the pattern analyzer 216 as described above.
[0086] The signal discriminator 214 may determine whether each of
the sampling data stored in the sampling data DB 221 is a normal
signal or an abnormal signal and generate labeling information for
each of the determined sampling data.
[0087] Here, as for the labeling information, for example, if
certain sampling data is determined as a normal signal, normal
signal labeling information Sn may be generated for the
corresponding sampling data. In addition, if certain sampling data
is determined as an abnormal signal, abnormal signal labeling
information Sa may be generated for the corresponding sampling
data.
[0088] As described above, the normal signal labeling information
and abnormal signal labeling information determined by the signal
discriminator 214 for a plurality of sampling data may be updated
and stored in the detected signal information DB 223.
[0089] As an example, the signal discriminator 214 may apply the
K-mean algorithm of unsupervised learning among machine learning
techniques to determine whether a plurality of sampling data is a
normal signal or an abnormal signal for. However, the present
disclosure is not limited to the application of machine learning
described above.
[0090] In order to further improve the accuracy of determination of
the machine learning technology for the sampling data, in the
actual application of the machine learning technology, training and
testing may be performed to repeatedly determine whether each of
the sampling data stored in the sampling data DB 221 is a normal
signal or an abnormal signal.
[0091] As described above, the signal discriminator 214 may
determine whether each of the sampling data stored in the sampling
data DB 221 is an abnormal signal, generate labeling information,
and store the generated labeling information in the detected signal
information DB 223.
[0092] The abnormal signal analyzer 215 may analyze a kind and
level of an abnormality type for the sampling data corresponding to
the abnormal signal labeling information determined as the abnormal
signal by the signal discriminator 214 and generate abnormal signal
analysis information.
[0093] Accordingly, the abnormal signal analysis information
generated by the abnormal signal analyzer 215 may include values
obtained by analyzing a kind and level of the abnormality type for
the corresponding sampling data.
[0094] As described above, when the anomaly signal analyzer 215
analyzes sampling data, deep learning algorithms may be
applied.
[0095] The pattern analyzer 216 may arrange the normal signal
labeling information among information determined by the signal
discriminator 214 and stored in the DB unit 220 and the abnormal
signal analysis information for sampling data of the abnormal
signal labeling information analyzed by the abnormal signal
analyzer 215 in a time series manner and perform pattern analysis
thereon.
[0096] When the pattern analyzer 216 performs pattern analysis, a
pre-installed pattern analysis program may be used. For example,
among hidden Markov model (HMM) and a deep learning model, a
recurrent neural network specified for time series data analysis
may be used as a learning model and at least one of long-short term
memories (LSTM) may be applied for data pattern learning in various
cases by utilizing information of long-term or short-term data.
However, the present disclosure is not limited thereto.
[0097] As described above, the pattern analyzer 216 analyzes the
pattern information using the normal signal labeling information
and the abnormal signal analysis information, whereby a level of
facility failure may be determined and a cause of the facility
failure may be determined.
[0098] The output module 217 may provide failure prediction
information for the facility 10 using the level and cause of the
facility failure analyzed by the pattern analyzer 216.
[0099] Hereinafter, an example of a method for operating a facility
failure prediction system will be described.
[0100] FIGS. 4 to 6 are views illustrating an example of a facility
failure prediction system according to an embodiment of the present
disclosure.
[0101] Specifically, FIG. 4 is a flowchart illustrating a process
of a facility failure prediction method according to an embodiment
of the present disclosure, FIG. 5 is a view specifically
illustrating a signal determination step S4, an abnormal signal
analysis step S5, and a pattern analysis step S6 illustrated in
FIG. 4, and FIG. 6 is a view illustrating a signal flow when a
facility failure prediction method is performed on a facility
failure prediction system according to an embodiment of the present
disclosure.
[0102] As illustrated in FIG. 4, the facility failure prediction
method according to an embodiment of the present disclosure may
include a sound wave collection step S1, an ultrasonic band
filtering step S2, a sampling data extraction step S3, a signal
determination step S4, an abnormal signal analysis step S5, a
pattern analysis step S6, and an output step S7.
[0103] In the sound wave collection step S1, the detection sensor
100 may collect acoustic signals from sound generated when the
facility 10 operates. Here, the collected acoustic signal may be
analog and may be converted into a digital signal.
[0104] In the sound wave collection step S1, when the collected
acoustic signal is converted into a digital signal, a sampling rate
for converting the acoustic signal into a digital signal may be 35
kHz to 300 kHz, preferably 100 kHz to 300 kHz, and, more preferably
190 kHz to 300 kHz. In the ultrasound band filtering step S2, an
ultrasound band is filtered from the acoustic signal which has been
converted into a digital signal, so that an acoustic signal of the
ultrasound band may be extracted.
[0105] In the sampling data extraction step S3, sampling data may
be extracted by canceling noise from the extracted ultrasonic band
signal.
[0106] As an example, in the noise filtering method, noise signal
information for environment noise or background noise may be
obtained in advance before the acoustic signal is collected and
applied as a noise filter for the corresponding acoustic signal.
However, the noise filtering method of the present disclosure is
not limited thereto and any other method may also be performed.
[0107] The sampling data extracted in the sampling data extraction
step may be data subdivided based on a predetermined time.
[0108] As described above, the sampling data subdivided based on
the predetermined time may be stored in the sampling data DB 221 as
shown in FIG. 6. As described above, the sampling data stored in
the sampling data DB 221 may include information on a waveform of
the corresponding signal and information on a generation time of
the sampling data.
[0109] In the signal extraction step S4, the signal discriminator
214 may receive the sampling data from the sampling data DB 221,
generate labeling information by determining whether the sampling
data is a normal signal or an abnormal signal, and store the
generated labeling information in the detected signal information
DB 223.
[0110] Here, the signal discriminator 214 may determine whether the
plurality of sampling signals are normal signals or abnormal
signals by applying, for example, a K-mean algorithm of
unsupervised learning among machine learning techniques. However,
the present disclosure is not limited to the application of machine
learning described above.
[0111] The labeling information stored in the detected signal
information DB 223 may include labeling information on whether the
sampling data is a normal signal or an abnormal signal and
identification information or time information on the sampling data
itself.
[0112] In addition, the labeling information may include normal
signal labeling information Sn indicating that the sampling data is
a normal signal and abnormal signal labeling information Sa
indicating that the sampling data is an abnormal signal.
[0113] As an example, in the signal determination step S4, as
illustrated in FIG. 6, it is determined whether the plurality of
sampling data stored in the sampling data DB 221 is a normal signal
or an abnormal signal to generate, for example, labeling
information determined such that sampling data 1 is normal signal
labeling information Sn, sampling data 2 is normal signal labeling
information Sn, and sampling data 3 is abnormal signal labeling
information Sa. Although not shown, each labeling information may
include identification information for corresponding sampling data
itself.
[0114] In the signal determination step S4, as shown in FIGS. 5 and
6, as for the labeling information stored in the detected signal
information DB 223, whether the labeling information for the
sampling data is the normal signal labeling information Sn or the
abnormal signal labeling information Sa is determined, and if the
labeling information is the normal signal labeling information Sn
(S41 in FIG. 5), the controller 210 may output the normal signal
labeling information Sn stored in the detected signal information
DB 223 to the pattern analyzer 216.
[0115] In addition, when the labeling information is the abnormal
signal labeling information Sa (S42 in FIG. 5), the controller 210
may output sampling data of the sampling data DB 221 corresponding
to the abnormal signal labeling information Sa stored in the
detected signal information DB 223 to the abnormal signal analyzer
215.
[0116] That is, as shown in FIG. 6, when the labeling information
is the abnormal signal labeling information Sa, the controller 210
may output the corresponding sampling data of the abnormal signal
labeling information Sa to the abnormal signal analyzer 215, so
that the abnormal signal analysis step S5 may be performed to more
closely analyze the corresponding sampling data.
[0117] In the abnormal signal analysis step S5, the sampling data
corresponding to the abnormal signal labeling information Sa among
the labeling information may be received and analyze to generate
abnormal signal analysis information.
[0118] For example, as shown in FIG. 6, in the signal determination
step S4, when the sampling data 3 is determined as an abnormal
signal, the sampling data 3 corresponding to the abnormal signal
labeling information Sa may be output from the sampling data DB 221
and input to the abnormal signal analyzer 215.
[0119] Here, the sampling data 3 may have a row-data form during a
predetermined time reference, and the abnormal signal analyzer 215
may analyze the raw data included in the sampling data 3.
[0120] The abnormal signal analyzer 215 may apply a deep learning
algorithm (Deep Learning Algorithms) to analyze the sampling data
determined as an abnormal signal.
[0121] As described above, the abnormal signal analysis information
generated by analyzing the sampling data in the abnormal signal
analyzer 215 may include values obtained by analyzing a kind and
level of the abnormality type for the sampling data corresponding
to the abnormal signal labeling information Sa.
[0122] For example, the abnormal signal analyzer 215 may generate
abnormal signal analysis information such as R1-50%, R3-60%, and
R5-30% as the abnormal signal analysis information. The abnormal
signal analyzer 215 may generate a kind and level of one or more
abnormality types for one sampling data.
[0123] For example, regarding sampling data 3, an abnormality type
kind 1 and R1-50% indicating a level of 50% may be generated as
abnormal signal analysis information. Accordingly, R1-50%, R3-60%,
and R5-30% may refer to abnormal signal analysis information
analyzed for each of the three sampling data.
[0124] Here, R1, R3, and R5 may refer to kinds of abnormality
types. That is, R1, R3, and R5 may refer to types that generate the
corresponding sampling data determined as an abnormal signal. Such
abnormality types may be one of several causes of facility
failure.
[0125] In addition, the percentage linked to each of R1-50%,
R3-60%, and R5-30% may refer to a level or magnitude of the
abnormality type.
[0126] Accordingly, R3-60% may mean that it corresponds to
abnormality type 3 and a level thereof is 60%, and R5-30% may mean
that it corresponds to abnormality type 5 and a level 30%.
[0127] As described above, the abnormality type analyzed by the
abnormal signal analyzer 215 may be set to be different for each
facility and may be changed by the administrator or the user.
[0128] As described above, the abnormal signal analysis information
analyzed by the abnormal signal analyzer 215 in the abnormal signal
analysis step S5 may be output to the pattern analyzer 216, and
thereafter, the pattern analysis step S6 may be performed.
[0129] In the pattern analysis step S6, the pattern analyzer 216
may receive normal signal labeling information Sn determined as a
normal signal from among a plurality of labeling information from
the detected signal information DB 223 and receive the abnormal
signal analysis information from the abnormal signal analyzer 215,
and analyze a pattern of facility failure.
[0130] To this end, the pattern analyzer 216 may arrange the normal
signal labeling information Sn and the abnormal signal analysis
information in a time-series manner in a time-ordered sequence.
[0131] For example, a plurality of normal signal labeling
information Sn and abnormal signal analysis information (R1-50%,
R3-60%, R5-30%, and R3-60%) may be arranged in a time-series manner
in order of R1-50%, Sn, R3-60%, R5-30%, R3-60%, . . . , Sn, Sn, and
the arranged pattern may be analyzed by the pattern analyzer
216.
[0132] As described above, the pattern arranged in the time series
manner may be information obtained by analyzing characteristics of
the signals for the respective sampling data input in chronological
order.
[0133] The pattern analyzer 216 may analyze the time-series pattern
of the normal signal labeling information Sn and the abnormal
signal analysis information to determine a level of facility
failure and a cause of the facility failure.
[0134] When the pattern analyzer 216 analyzes the time series
pattern, among hidden Markov model (HMM) and a deep learning model,
a recurrent neural network specified for time series data analysis
may be used as a learning model and at least one of long-short term
memories (LSTM) may be applied for data pattern learning in various
cases by utilizing information of long-term or short-term data.
However, the present disclosure is not limited thereto.
[0135] In the output step S7, the failure level of the facility and
the cause of the facility failure analyzed by the pattern analyzer
216 may be output as failure prediction information of the
facility.
[0136] FIG. 7 is a view illustrating an example of failure
prediction information provided by a facility failure prediction
system according to an embodiment of the present disclosure.
[0137] The facility failure prediction system according to an
embodiment of the present disclosure may provide failure prediction
information as illustrated in FIG. 7.
[0138] Specifically, the failure prediction information may include
at least a current state of the facility 10 and prediction
information until failure of the facility 10 occurs.
[0139] As an example, as shown in FIG. 7, as the failure prediction
information, a name of the facility 10, prediction information
indicating a probability of failure until it occurs in percent, the
number of occurrences of a normal signal, the number of occurrences
of an abnormal signal, a level of each kind of an abnormality type,
an occurrence interval, a predicted percentage up to the failure of
the facility 10 may be displayed.
[0140] The facility failure prediction system and method according
to an embodiment of the present disclosure provide failure
prediction information on the facility 10 using an ultrasonic band
signal in an acoustic signal generated during the operation of the
facility 10, so that the user may be guided to promptly respond to
the facility 10 in the factory, thereby minimizing a cost of damage
caused by the shutdown of the factory.
[0141] The present disclosure is not limited to the above-described
embodiments and the accompanying drawings, and various
modifications and variations may be made from the viewpoint of a
person having ordinary skills in the art to which the present
disclosure pertains. Therefore, the scope of the present disclosure
should be defined not only by the claims of the present
specification, but also by the claims and equivalents thereof.
* * * * *